A beginner’s guide to understanding A/B test performance through Monte Carlo simulations

This tutorial explores how covariates influence A/B testing precision in a randomized experiment. A properly randomized A/B test calculates the lift by comparing the average outcome in the treatment and control groups. However, the influence of features other than the treatment on the outcome determines the statistical properties of the A/B test. For instance, omitting influential features in the test lift calculation can lead to a highly imprecise estimate of the lift, even if it converges to the true value …

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